在基于稳态视觉诱发电位的脑机接口应用中使用深度学习技术的系统性综述:当前趋势与未来信任方法》。

IF 3.1 Q2 HEALTH CARE SCIENCES & SERVICES
International Journal of Telemedicine and Applications Pub Date : 2023-04-30 eCollection Date: 2023-01-01 DOI:10.1155/2023/7741735
A S Albahri, Z T Al-Qaysi, Laith Alzubaidi, Alhamzah Alnoor, O S Albahri, A H Alamoodi, Anizah Abu Bakar
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引用次数: 0

摘要

本研究通过系统性综述评估了深度学习技术对基于稳态视觉诱发电位(SSVEP)的脑机接口(BCI)应用的意义。为了收集相关的科学和理论文章,我们考虑了三个可靠的数据库:PubMed、ScienceDirect 和 IEEE。最初,我们找到了 2010 年至 2021 年期间与这一综合研究领域相关的 125 篇论文。经过筛选,只确定了 30 篇文章,并根据其深度学习方法的类型分为五类。第一类是卷积神经网络(CNN),占 70%(n = 21/30)。第二类是循环神经网络(RNN),占 10%(n = 3/30)。第三类和第四类,即深度神经网络(DNN)和长短期记忆(LSTM),占 6%(n = 30)。第五类是受限波尔兹曼机(RBM),占 3%(n = 1/30)。文献的研究结果涉及深度学习模式识别技术在基于SSVEP的BCI中的现有应用中发现的主要方面,如特征提取、分类、激活函数、验证方法和达到的分类精度。此外,还进行了全面的映射分析,确定了六个类别。研究讨论了当前在基于 SSVEP 的生物识别应用中确保可信深度学习所面临的挑战,并向研究人员和开发人员提出了建议。研究从基于深度学习技术的开发挑战和基于多标准决策(MCDM)的选择挑战两个方面,批判性地回顾了基于 SSVEP 的生物识别(BCI)应用目前尚未解决的问题。研究提出了一种信任建议解决方案,包括三个方法论阶段,用于使用模糊决策技术评估和基准测试基于 SSVEP 的生物识别(BCI)应用。为基于 SSVEP 的生物识别和深度学习领域的研究人员和开发人员提供了宝贵的见解和建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology.

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.

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来源期刊
CiteScore
6.90
自引率
2.30%
发文量
19
审稿时长
12 weeks
期刊介绍: The overall aim of the International Journal of Telemedicine and Applications is to bring together science and applications of medical practice and medical care at a distance as well as their supporting technologies such as, computing, communications, and networking technologies with emphasis on telemedicine techniques and telemedicine applications. It is directed at practicing engineers, academic researchers, as well as doctors, nurses, etc. Telemedicine is an information technology that enables doctors to perform medical consultations, diagnoses, and treatments, as well as medical education, away from patients. For example, doctors can remotely examine patients via remote viewing monitors and sound devices, and/or sampling physiological data using telecommunication. Telemedicine technology is applied to areas of emergency healthcare, videoconsulting, telecardiology, telepathology, teledermatology, teleophthalmology, teleoncology, telepsychiatry, teledentistry, etc. International Journal of Telemedicine and Applications will highlight the continued growth and new challenges in telemedicine, applications, and their supporting technologies, for both application development and basic research. Papers should emphasize original results or case studies relating to the theory and/or applications of telemedicine. Tutorial papers, especially those emphasizing multidisciplinary views of telemedicine, are also welcome. International Journal of Telemedicine and Applications employs a paperless, electronic submission and evaluation system to promote a rapid turnaround in the peer-review process.
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